Import packages used in this notebook

import collections
import numpy as np
import os
import pandas as pd
import plotly.graph_objects as go
import matplotlib
import matplotlib.pyplot as plt

from sklearn.decomposition import PCA

# Define the core working directory, and the output path for files and plots:
core_path = '/data/gardeux'
output_path = os.path.join(core_path, 'output')
output_path

'/data/gardeux/output'

1. 1000G ethnicities

a) Uploading the ethnicity file

ethnicities_df = pd.read_csv(  # pd.read_csv() reads the file
    os.path.join(core_path, 'ethnicities.txt'),  # specify the file name
    sep='\t',  # specity separator
    header=None  # indicate that file has no header
)

ethnicities_df.head()

	0	1	2	3	4
0	HG00097	GBR	British in England and Scotland	EUR	European
1	HG00099	GBR	British in England and Scotland	EUR	European
2	HG00100	GBR	British in England and Scotland	EUR	European
3	HG00101	GBR	British in England and Scotland	EUR	European
4	HG00103	GBR	British in England and Scotland	EUR	European

b), c), d) Plotting the 3rd, 4th and 5th columns as a barplot to summarize the number of samples per category

def get_barplot_for_counts(
        input_dict,
        save_plot=False,
        output_path=None,
        output_file_name=None
    ):
    '''
    This function creates a barplot based on
    the input dictionary with numeric values,
    and outputs a barplot, which if needed is
    saved into a .pdf.
    
    Parameters:
        * input_dict -- input dictionary with numeric values;
        * save_plot -- boolean value indicating whether the plot
        should be saved to file;
        * output_path -- path to the output directory;
        * output_file_name -- name of the file (with extension)
        where the barplot will be saved.
    '''
    input_dict = dict(
        sorted(input_dict.items(), key=lambda item: item[1])
    )  # sort dictionary by values in an increasing order
    
    fig, ax = plt.subplots(1, 1, figsize=(5, 5))  # initialize the figure
    ax.bar(
        input_dict.keys(),  # x values
        input_dict.values(),  # y values
        color='gray',  # change the default color
        alpha=0.5  # set opacity
    )
    #ax.set_xticklabels(input_dict.keys())
    ax.tick_params(axis='x', labelrotation = 90)
    ax.set_ylabel('Counts', size=12)
    
    # Removing top and right spines is optional:
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    
    if save_plot:
        # If True, check if the plots directory exists in the output folder.
        # If not, create this directory.
        if not os.path.exists(os.path.join(output_path, 'plots')):
            os.makedirs(os.path.join(output_path, 'plots'))
        # save figure
        plt.savefig(
            os.path.join(
                output_path,
                'plots',
                output_file_name),  # specify the file name
            dpi=300,  # set the resolution of a plot
            bbox_inches='tight',  # remove extra white space around the figure
            transparent=True  # remove white background
        )

get_barplot_for_counts(
    # Get the third column of the ethnicities dataframe and
    # get counts per each category with the collections.Counter function:
    dict(collections.Counter(ethnicities_df.iloc[:, 2])),
    save_plot=True,
    output_path=output_path,
    output_file_name='barplot_col2.pdf'
)

# Create similar plot for the 4th column
get_barplot_for_counts(
    dict(collections.Counter(ethnicities_df.iloc[:, 3])),
    save_plot=True,
    output_path=output_path,
    output_file_name='barplot_col3.pdf',
)

# Create similar plot for the 5th column
get_barplot_for_counts(
    dict(collections.Counter(ethnicities_df.iloc[:, 4])),
    save_plot=True,
    output_path=output_path,
    output_file_name='barplot_col4.pdf'
)

2. Reading the VCF file

Check with the %%bash magic function header of the file:

%%bash
head -n 5 /data/gardeux/genotypes.vcf
# We need to skip 2 top lines sterting with ##

##fileformat=VCFv4.0
##fileDate=20181031
#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	LA001	LA002	LA003	LA004	LA005	LA006	LA007	LA008	LA009	LA010	LA011	LA012	LA013	LA014	LA015	LA016	LA017	LA018	LA019	LA020	LA021	LA022	LA023	LA024	LA025	LA026	LA027	LA028	LA029	LA030	LA031	LA032	LA033	LA034	LA035	LA036	LA037	LA038	LA039	LA040	LA041	LA042	LA043	LA044	LA045	LA046	LA047	LA048	LA049	LA050	HG00097	HG00099	HG00100	HG00101	HG00103	HG00105	HG00106	HG00107	HG00108	HG00109	HG00110	HG00111	HG00112	HG00113	HG00115	HG00116	HG00117	HG00118	HG00119	HG00120	HG00121	HG00122	HG00123	HG00126	HG00127	HG00128	HG00130	HG00131	HG00132	HG00133	HG00136	HG00137	HG00138	HG00139	HG00140	HG00141	HG00142	HG00145	HG00148	HG00149	HG00150	HG00151	HG00154	HG00158	HG00159	HG00171	HG00173	HG00174	HG00176	HG00177	HG00178	HG00179	HG00180	HG00181	HG00182	HG00183	HG00185	HG00187	HG00188	HG00189	HG00190	HG00231	HG00232	HG00233	HG00234	HG00235	HG00236	HG00237	HG00238	HG00239	HG00240	HG00242	HG00244	HG00245	HG00246	HG00250	HG00251	HG00252	HG00253	HG00254	HG00255	HG00256	HG00257	HG00258	HG00261	HG00262	HG00263	HG00264	HG00265	HG00266	HG00267	HG00268	HG00269	HG00271	HG00272	HG00273	HG00274	HG00275	HG00276	HG00277	HG00278	HG00280	HG00281	HG00282	HG00284	HG00285	HG00288	HG00290	HG00304	HG00306	HG00308	HG00309	HG00310	HG00313	HG00315	HG00318	HG00319	HG00320	HG00321	HG00323	HG00324	HG00325	HG00326	HG00327	HG00328	HG00330	HG00331	HG00332	HG00334	HG00335	HG00336	HG00337	HG00338	HG00339	HG00341	HG00342	HG00343	HG00344	HG00345	HG00346	HG00349	HG00351	HG00353	HG00355	HG00356	HG00357	HG00358	HG00360	HG00361	HG00362	HG00364	HG00365	HG00366	HG00367	HG00368	HG00369	HG00371	HG00372	HG00373	HG00375	HG00376	HG00378	HG00380	HG00381	HG00382	HG00383	HG00384	HG00403	HG00404	HG00406	HG00407	HG00409	HG00410	HG00419	HG00421	HG00422	HG00428	HG00436	HG00437	HG00442	HG00443	HG00445	HG00446	HG00448	HG00449	HG00451	HG00452	HG00457	HG00458	HG00463	HG00464	HG00472	HG00473	HG00475	HG00476	HG00478	HG00479	HG00500	HG00513	HG00524	HG00525	HG00530	HG00531	HG00533	HG00534	HG00536	HG00537	HG00542	HG00543	HG00551	HG00553	HG00554	HG00556	HG00557	HG00559	HG00560	HG00565	HG00566	HG00580	HG00581	HG00583	HG00584	HG00589	HG00590	HG00592	HG00593	HG00595	HG00596	HG00598	HG00599	HG00607	HG00608	HG00610	HG00611	HG00613	HG00614	HG00619	HG00620	HG00622	HG00623	HG00625	HG00626	HG00628	HG00629	HG00631	HG00632	HG00634	HG00637	HG00638	HG00640	HG00641	HG00650	HG00651	HG00653	HG00654	HG00656	HG00657	HG00662	HG00663	HG00671	HG00672	HG00674	HG00675	HG00683	HG00684	HG00689	HG00690	HG00692	HG00693	HG00698	HG00699	HG00701	HG00704	HG00707	HG00708	HG00717	HG00728	HG00729	HG00731	HG00732	HG00734	HG00736	HG00737	HG00739	HG00740	HG00742	HG00743	HG00759	HG00766	HG00844	HG00851	HG00864	HG00867	HG00879	HG00881	HG00956	HG00978	HG00982	HG01028	HG01029	HG01031	HG01046	HG01047	HG01048	HG01049	HG01051	HG01052	HG01054	HG01055	HG01058	HG01060	HG01061	HG01063	HG01064	HG01066	HG01067	HG01069	HG01070	HG01072	HG01073	HG01075	HG01077	HG01079	HG01080	HG01082	HG01083	HG01085	HG01086	HG01088	HG01089	HG01092	HG01094	HG01095	HG01097	HG01098	HG01101	HG01102	HG01104	HG01105	HG01107	HG01108	HG01110	HG01111	HG01112	HG01113	HG01119	HG01121	HG01122	HG01124	HG01125	HG01130	HG01131	HG01133	HG01134	HG01136	HG01137	HG01139	HG01140	HG01142	HG01148	HG01149	HG01161	HG01162	HG01164	HG01167	HG01168	HG01170	HG01171	HG01173	HG01174	HG01176	HG01177	HG01182	HG01183	HG01187	HG01188	HG01190	HG01191	HG01197	HG01198	HG01200	HG01204	HG01205	HG01241	HG01242	HG01247	HG01248	HG01250	HG01251	HG01253	HG01254	HG01256	HG01257	HG01259	HG01260	HG01269	HG01271	HG01272	HG01275	HG01277	HG01280	HG01281	HG01284	HG01286	HG01302	HG01303	HG01305	HG01308	HG01311	HG01312	HG01323	HG01325	HG01326	HG01334	HG01341	HG01342	HG01344	HG01345	HG01348	HG01350	HG01351	HG01353	HG01354	HG01356	HG01357	HG01359	HG01360	HG01362	HG01363	HG01365	HG01366	HG01369	HG01372	HG01374	HG01375	HG01377	HG01378	HG01383	HG01384	HG01389	HG01390	HG01392	HG01393	HG01395	HG01396	HG01398	HG01402	HG01403	HG01405	HG01412	HG01413	HG01414	HG01431	HG01432	HG01435	HG01437	HG01438	HG01440	HG01441	HG01443	HG01444	HG01447	HG01455	HG01456	HG01459	HG01461	HG01462	HG01464	HG01465	HG01468	HG01474	HG01479	HG01485	HG01486	HG01488	HG01489	HG01491	HG01492	HG01494	HG01495	HG01497	HG01498	HG01500	HG01501	HG01503	HG01504	HG01506	HG01507	HG01509	HG01510	HG01512	HG01513	HG01515	HG01516	HG01518	HG01519	HG01521	HG01522	HG01524	HG01525	HG01528	HG01530	HG01536	HG01537	HG01550	HG01551	HG01556	HG01565	HG01566	HG01571	HG01572	HG01577	HG01578	HG01583	HG01586	HG01589	HG01593	HG01595	HG01596	HG01597	HG01598	HG01599	HG01600	HG01602	HG01603	HG01605	HG01606	HG01608	HG01610	HG01612	HG01613	HG01615	HG01617	HG01618	HG01619	HG01620	HG01623	HG01624	HG01625	HG01626	HG01628	HG01630	HG01631	HG01632	HG01668	HG01670	HG01672	HG01673	HG01675	HG01676	HG01678	HG01679	HG01680	HG01682	HG01684	HG01685	HG01694	HG01695	HG01697	HG01699	HG01700	HG01702	HG01704	HG01705	HG01707	HG01708	HG01709	HG01710	HG01746	HG01747	HG01756	HG01757	HG01761	HG01762	HG01765	HG01766	HG01767	HG01768	HG01770	HG01771	HG01773	HG0177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1	1218086	rs6603788	C	T	.	.	.	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|0	1|1	1|1	1|1	0|1

# Read the genotypes data
#types = {"#CHROM":str, "POS":np.int64, "ID":str, "REF":str, "ALT":str, "QUAL":str, "FILTER":str, "INFO":str, "FORMAT":str}
genotypes_df = pd.read_csv(
    os.path.join(core_path, 'genotypes.vcf'),  # specify the file name
    sep='\t',  # specify separator
    dtype={"POS":np.int64}, # [Optional] To specify the type of column 'POS'. The rest will be treated as str
    skiprows=2  # skip the first two rows with two hashes (##)
)

/usr/local/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3169: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

3. Merge both ethnic and VCF datasets

a) Get samples with missing ethnicity annotation

# get samples from the ethnicity dataframe:
ethnicity_samples = set(ethnicities_df.iloc[:, 0])
# get samples from the genotype dataframe:
genotype_samples = set(genotypes_df.iloc[:, 9:].columns)

# check lengths of the sample sets
if sorted(ethnicity_samples) != sorted(genotype_samples):
    print('The sample sets are different')
    print('# samples with ethnicities:', len(ethnicity_samples))
    print('# samples with genotypes:', len(genotype_samples))

The sample sets are different
# samples with ethnicities: 2454
# samples with genotypes: 2504

# get missing samples
missing_samples = genotype_samples - ethnicity_samples
print('There are', len(missing_samples), 'samples with missing ethnicies')

There are 50 samples with missing ethnicies

b) Merge the ethnicities dataframe with the one with samples having missing annotations

# subset the initial dataframe by sample id and ethnicity columns:
ethnicities_subset_df = ethnicities_df[[0, 3]]
ethnicities_subset_df.columns = ['sample_id', 'ethnicity']  # set column names

# create a new dataframe with missing samples and ethnicities set to NAN
missing_samples_df = pd.DataFrame(
    [[missing_sample, 'Unknown']
     for missing_sample in missing_samples],
     columns=['sample_id', 'ethnicity']
)

# merge subsetted version of the ethnicities dataframe
# with the one with missing samples:
merged_ethn_df = pd.concat(
    [ethnicities_subset_df, missing_samples_df],
    axis=0
)

c) Generate a pie chart of the final ethnicities, including the 'Unknown' samples

# Express ethnicities contribution as percentages to the full pool of sample ethnicities
ethnicity_pct_dict = {
    ethnicity_: ethn_df.shape[0] / merged_ethn_df.shape[0] * 100
    for ethnicity_, ethn_df in merged_ethn_df.groupby('ethnicity')
}

# Get a colour map
cmap = matplotlib.cm.get_cmap('Set3')
cmap

Set3

under

bad

over

fig, ax = plt.subplots(1, 1, figsize=(3, 3))  # initialize the figure
ax.pie(
    x=list(ethnicity_pct_dict.values()),  # values for the pie chart
    labels=ethnicity_pct_dict.keys(),  # annotations per wedge
    autopct='%.1f%%',  # Automatically add % symbol and round the value to the first decimal
    startangle=90,  # start drawing the pie chart from this angle
    colors=[cmap(i) for i in range(len(ethnicity_pct_dict))]  # set color for each wedge
)
ax.axis('equal')  # set equal axes s.t. the pie is curcular

# Save figure in file
pie_path=os.path.join(output_path, 'plots', 'pie_chart.png')
plt.savefig(
            pie_path,  # specify the file name
            dpi=300,  # set the resolution of a plot
            bbox_inches='tight',  # remove extra white space around the figure
            transparent=True  # remove white background
        )
print("Pie chart saved in " + pie_path)

Pie chart saved in /data/gardeux/output/plots/pie_chart.png

4. Prepare the data for the PCA

a) Create a dataframe containing only genotypes

df_for_pca = genotypes_df.iloc[:, 9:]
df_for_pca.head()

	LA001	LA002	LA003	LA004	LA005	LA006	LA007	LA008	LA009	LA010	...	NA21128	NA21129	NA21130	NA21133	NA21135	NA21137	NA21141	NA21142	NA21143	NA21144
0	1|1	0|1	0|1	0|0	0|0	0|0	0|0	1|0	0|0	0|0	...	0|0	0|1	0|1	0|1	0|0	1|0	0|1	1|1	1|1	0|0
1	0|1	0|1	1|1	0|1	1|0	0|0	0|0	1|0	0|1	1|0	...	1|1	1|1	1|1	0|1	0|0	1|0	1|1	1|1	1|1	0|1
2	1|1	0|0	0|1	1|1	1|0	0|0	1|1	0|1	0|0	0|0	...	0|0	0|0	0|0	1|1	1|1	0|0	0|0	0|0	0|1	0|1
3	1|0	0|0	0|0	0|0	1|1	0|0	1|1	1|0	1|0	0|1	...	0|1	0|1	0|1	0|0	0|0	0|1	1|0	1|0	0|1	0|0
4	1|1	0|1	0|0	0|0	0|0	0|0	1|0	0|1	0|0	0|0	...	0|0	1|0	0|0	1|1	0|0	1|1	0|0	0|0	0|1	0|0

5 rows × 2504 columns

b) Transforming the data into numerical format

df_for_pca.replace('0|0', 0, inplace=True)
df_for_pca.replace('0|1', 1, inplace=True)
df_for_pca.replace('1|0', 1, inplace=True)
df_for_pca.replace('1|1', 2, inplace=True)

# To make sure that each value in the dataframe has one data type
# set dataframe type int
df_for_pca = df_for_pca.astype(int)

df_for_pca.head()

	LA001	LA002	LA003	LA004	LA005	LA006	LA007	LA008	LA009	LA010	...	NA21128	NA21129	NA21130	NA21133	NA21135	NA21137	NA21141	NA21142	NA21143	NA21144
0	2	1	1	0	0	0	0	1	0	0	...	0	1	1	1	0	1	1	2	2	0
1	1	1	2	1	1	0	0	1	1	1	...	2	2	2	1	0	1	2	2	2	1
2	2	0	1	2	1	0	2	1	0	0	...	0	0	0	2	2	0	0	0	1	1
3	1	0	0	0	2	0	2	1	1	1	...	1	1	1	0	0	1	1	1	1	0
4	2	1	0	0	0	0	1	1	0	0	...	0	1	0	2	0	2	0	0	1	0

5 rows × 2504 columns

5. Running PCA and visualizing the results

a. Perform the PCA

pca_array = PCA(random_state=42).fit_transform(df_for_pca.T)
pca_df = pd.DataFrame(pca_array, index=df_for_pca.columns)

b) Visualize the first two components of the PCA and color samples by ethnicity

## Add a new column to the merged_ethn_df dataframe with colors based on sample ethnicity

# We can do it with random colors from the cmap() color panel:
#merged_ethn_df['color'] = merged_ethn_df['ethnicity'].copy().map(
#    {
#        ethn_: cmap(i) for i, ethn_ in enumerate(set(merged_ethn_df['ethnicity']))
#    }
#)

# Or using our own color panel:
# Check here for color names: https://matplotlib.org/stable/gallery/color/named_colors.html
merged_ethn_df['color'] = merged_ethn_df['ethnicity'].copy().map({'AFR':'red', 'EAS':'palegreen', 'SAS':'chartreuse', 'EUR':'deepskyblue', 'AMR':'pink', 'Unknown':'black'})

# Prepare the color dictionary, where each sample has the respective color
# based on its ethnicity :
ethn_color_dict = dict(zip(merged_ethn_df['sample_id'], merged_ethn_df['color']))

fig, ax = plt.subplots(1, 1, figsize=(5, 5))  # initialize the figure
ax.scatter(
    pca_df.loc[merged_ethn_df['sample_id'], 0],  # specify the first component
    pca_df.loc[merged_ethn_df['sample_id'], 1],  # specify the second component
    color=ethn_color_dict.values(),  # specify sample colors
    alpha=0.75,  # set opacity of points
    linewidths=0.3,  # set thickness of boundaries for points
    edgecolors='black'  # set color of boundaries for points
)
# Optional: remove top and right spines from the figure
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

c) Visualize the first three components of the PCA in 3D, color samples by ethnicity and save the result

fig = plt.figure(1, figsize=(8, 8))  # initialize the figure
ax = fig.add_subplot(111, projection='3d')  # specify the dimension of the plot
ax.scatter(
    pca_df.loc[merged_ethn_df['sample_id'], 0],  # specify the first component
    pca_df.loc[merged_ethn_df['sample_id'], 1],  # specify the first component
    pca_df.loc[merged_ethn_df['sample_id'], 2],  # specify the third component
    color=ethn_color_dict.values(),  # specify sample colors
    linewidths=0.3,  # set thickness of boundaries for points
    edgecolors='black'  # set color of boundaries for points
)
# Set axes labels
ax.set_xlabel('1st eigenvector')
ax.set_ylabel('2nd eigenvector')
ax.set_zlabel('3rd eigenvector')
plt.savefig(
    os.path.join(
        output_path,
        'plots',
        '3D_PCA.pdf'
    ),
    dpi=300,  # set the resolution of a plot
    bbox_inches='tight',  # remove extra white space around the figure
    transparent=True  # remove white background
    
)

d) Highlighting the Unknown samples and saving the figure

# Create a dictionary with opacity values depending on
# the sample annotation.
# If ethnicity is unknown, alpha=1, otherwise, alpha=0.3
alpha_intensities_dict = {}
for sample_id, ethn_ in zip(
    merged_ethn_df['sample_id'],
    merged_ethn_df['ethnicity']
):
    if ethn_ == 'Unknown':
        alpha_intensities_dict[sample_id] = 1
    else:
        alpha_intensities_dict[sample_id] = 0.3

# Note that the order of samples is important!
# Namely, we want the samples with Unknown ethnicities
# to be visualized on top of all other samples. In this
# way you make sure the samples are visible and not hidden
# behind other data points.

fig, ax = plt.subplots(1, 1, figsize=(5, 5))  # initialize the figure
ax.scatter(
    pca_df.loc[merged_ethn_df['sample_id'], 0],  # specify the first component
    pca_df.loc[merged_ethn_df['sample_id'], 1],  # specify the second component
    color=ethn_color_dict.values(),  # specify sample colors
    alpha=list(alpha_intensities_dict.values()),  # specify opacity of sample points
    linewidths=0.3,  # set thickness of boundaries for points
    edgecolors='black'  # set color of boundaries for points
)

# Optional: remove top and right spines from the figure
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.savefig(
    os.path.join(
        output_path,
        'plots',
        '2D_PCA_with_highlighted_unknowns.pdf'
    ),
    dpi=300,  # set the resolution of a plot
    bbox_inches='tight',  # remove extra white space around the figure
    transparent=True  # remove white background
    
)

d) Highlighting the Unknown samples with different markers and different sizes

# Prepare the merker dictionary, where each all samples have circular marker
# except the samples with unkown ethnicity which have triangles as markers:
marker_dict = {}
for sample_id, ethn_ in zip(
    merged_ethn_df['sample_id'],
    merged_ethn_df['ethnicity']
):
    if ethn_ == 'Unknown':
        marker_dict[sample_id] = '^'
    else:
        marker_dict[sample_id] = 'o'

fig, ax = plt.subplots(1, 1, figsize=(5, 5))  # initialize the figure
# Make a scatterplot for the samples with known ethnicities
ax.scatter(
    pca_df.loc[ethnicities_subset_df['sample_id'], 0],  # specify the first component
    pca_df.loc[ethnicities_subset_df['sample_id'], 1],  # specify the second component
    color=[
        color_
        for sample_, color_ in ethn_color_dict.items()
        if sample_ in list(ethnicities_subset_df['sample_id'])
    ],  # get colors for the samples with known ethnicities
    marker='o',  # specify marker
    alpha=0.3, # specify opacity of sample points
    linewidths=0.3,  # set thickness of boundaries for points
    edgecolors='black'  # set color of boundaries for points
)
# Make a scatterplot for the samples with Unknown ethnicities
ax.scatter(
    pca_df.loc[missing_samples_df['sample_id'], 0],  # specify the first component
    pca_df.loc[missing_samples_df['sample_id'], 1],  # specify the second component
    color=[
        color_
        for sample_, color_ in ethn_color_dict.items()
        if sample_ in list(missing_samples_df['sample_id'])
    ],  # get colors for the samples with inknown ethnicities
    marker='^',  # specify marker
    alpha=1, # specify opacity of sample points
    linewidths=0.3,  # set thickness of boundaries for points
    edgecolors='black'  # set color of boundaries for points
)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

Now let's change the size of markers for unknown samples:

fig, ax = plt.subplots(1, 1, figsize=(5, 5))  # initialize the figure
# Make a scatterplot for the samples with known ethnicities
ax.scatter(
    pca_df.loc[ethnicities_subset_df['sample_id'], 0],  # specify the first component
    pca_df.loc[ethnicities_subset_df['sample_id'], 1],  # specify the second component
    color=[
        color_
        for sample_, color_ in ethn_color_dict.items()
        if sample_ in list(ethnicities_subset_df['sample_id'])
    ],  # get colors for the samples with known ethnicities
    marker='o',  # specify marker
    alpha=0.3, # specify opacity of sample points
    linewidths=0.3,  # set thickness of boundaries for points
    edgecolors='black'  # set color of boundaries for points
)
# Make a scatterplot for the samples with Unknown ethnicities
ax.scatter(
    pca_df.loc[missing_samples_df['sample_id'], 0],  # specify the first component
    pca_df.loc[missing_samples_df['sample_id'], 1],  # specify the second component
    color=[
        color_
        for sample_, color_ in ethn_color_dict.items()
        if sample_ in list(missing_samples_df['sample_id'])
    ],  # get colors for the samples with inknown ethnicities
    marker='^',  # specify marker
    s=100,  # set marker size
    alpha=1, # specify opacity of sample points
    linewidths=0.3,  # set thickness of boundaries for points
    edgecolors='black'  # set color of boundaries for points
)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

6. Generating an interactive PCA

fig = go.Figure(  # initialize a plotly figure
    data=[
        go.Scatter3d(  # specify a 3D scatter plot
            x=pca_df.loc[merged_ethn_df['sample_id'], 0],  # specify the first component
            y=pca_df.loc[merged_ethn_df['sample_id'], 1],  # specify the second component
            z=pca_df.loc[merged_ethn_df['sample_id'], 2],  # specify the third component
            mode='markers',  # change the format of lines+markers to markers only
            marker=dict(  # define dictionary with marker parameters
                size=5,  # set marker size
                # convert the rgba colors into hex format:
                color=[
                    matplotlib.colors.rgb2hex(rgba)
                    for rgba in ethn_color_dict.values()
                ],
                opacity=0.75,  # specify opacity of sample points
                #line=dict(width=0.5, color='black')  # set thickness and color of boundaries for points
            ),
        )
    ]
)
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))  # make a tight layout
fig.show()  # show the figure